Presentation Open Access
A Non Linear Control Method with Reinforcement Learning for Adaptive Optics with Pyramid Sensors
Pou, Bartomeu;
Smith, Jeffrey;
Quinones, Eduardo;
Martin, Mario;
Gratadour, Damien
Extreme Adaptive Optics (AO) systems are designed to provide high resolution and high contrast observing capabilities on the largest ground-based telescopes through exquisite phase reconstruction accuracy. In that context, the pyramid wavefront sensor (P-WFS) has shown promise to deliver the means to provide such accuracy due to its high sensitivity. However, traditional methods cannot leverage the highly non-linear P-WFS measurements to their full potential. We present a predictive control method based on Reinforcement Learning (RL) for AO control with a P-WFS. The proposed approach is data-driven, has no assumptions about the system's evolution, and is non-linear due to the usage of neural networks. First, we discuss the challenges of using an RL control method with a P-WFS and propose solutions. Then, we show that our method outperforms an optimized integrator controller. Finally, we discuss its possible path for an actual implementation.
| Name | Size | |
|---|---|---|
|
Presentation_Bartomeu_Pou.pdf
md5:ffc4db95eeb58c4c43c57f69df9db982 |
1.3 MB | Download |
| All versions | This version | |
|---|---|---|
| Views | 134 | 134 |
| Downloads | 64 | 64 |
| Data volume | 82.8 MB | 82.8 MB |
| Unique views | 129 | 129 |
| Unique downloads | 59 | 59 |
- Publication date:
- May 20, 2022
- DOI:
-
- Keyword(s):
- Adaptive Optics Reinforcement Learning
- Grants:
-
European Commission:
-
Rising STARS - RISE International Network for Solutions Technologies and Applications of Real-time Systems (873120)
- AMPERE - A Model-driven development framework for highly Parallel and EneRgy-Efficient computation supporting multi-criteria optimisation (871669)
-
Rising STARS - RISE International Network for Solutions Technologies and Applications of Real-time Systems (873120)
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International